MILD: Multiple-Instance Learning via Disambiguation
نویسندگان
چکیده
منابع مشابه
Multiple-Instance Learning with Instance Selection via Dominant Sets
Multiple-instance learning (MIL) deals with learning under ambiguity, in which patterns to be classified are described by bags of instances. There has been a growing interest in the design and use of MIL algorithms as it provides a natural framework to solve a wide variety of pattern recognition problems. In this paper, we address MIL from a view that transforms the problem into a standard supe...
متن کاملMultiple-Instance Learning via Disjunctive Programming Boosting
Learning from ambiguous training data is highly relevant in many applications. We present a new learning algorithm for classification problems where labels are associated with sets of pattern instead of individual patterns. This encompasses multiple instance learning as a special case. Our approach is based on a generalization of linear programming boosting and uses results from disjunctive pro...
متن کاملRevisiting Multiple-Instance Learning Via Embedded Instance Selection
Multiple-Instance Learning via Embedded Instance Selection (MILES) is a recently proposed multiple-instance (MI) classification algorithm that applies a single-instance base learner to a propositionalized version of MI data. However, the original authors consider only one single-instance base learner for the algorithm — the 1-norm SVM. We present an empirical study investigating the efficacy of...
متن کاملMultiple-Instance Learning Via Random Walk
This paper presents a decoupled two stage solution to the multiple-instance learning (MIL) problem. With a constructed affinity matrix to reflect the instance relations, a modified Random Walk on a Graph process is applied to infer the positive instances in each positive bag. This process has both a closed form solution and an efficient iterative one. Combined with the Support Vector Machine (S...
متن کاملMultiple Instance Learning via Covariant Aggregation
We present a multiple instance learning (MIL) algorithm that learns ellipsoidal decision boundaries with arbitrary covariance. In contrast to the fixed-length feature vectors of traditional classification problems, MIL operates on unordered bags of instances. Commonly, each instance is a feature vector, and a bag is considered positive if any one of its instances is positive. In training data, ...
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ژورنال
عنوان ژورنال: IEEE Transactions on Knowledge and Data Engineering
سال: 2010
ISSN: 1041-4347
DOI: 10.1109/tkde.2009.58